Either a matrix or ExpressionSet containing
the training data. For a matrix, the rows are features, and the columns
are samples.
classes
A vector of class labels.
...
Unused variables from the matrix method passed to the
ExpressionSet method.
rows
A vector specifying which rows of the matrix to plot.
whichPlots
Which plots to draw. Can draw either a density plot, stripchart, or both.
xAxisLabel
The axis label for the expression axis.
yAxisLabels
A character vector of length 2. The first value is the y-axis label
for the density plot. The second value is the y-axis labels for the
stripchart. Provide both labels, even if only plotting one kind of plot.
expressionLimits
The minimum and maximum expression values to plot. Set to NULL to use range of data.
showXtickLabels
Logical. IF FALSE, the x-axis labels are hidden.
showYtickLabels
Logical. IF FALSE, the y-axis labels are hidden.
xLabelPositions
Either "auto" or a vector of values. The positions of labels on the x-axis.
If "auto", the placement of labels is automatically calculated.
yLabelPositions
Either "auto" or a vector of values. The positions of labels on the y-axis.
If "auto", the placement of labels is automatically calculated.
fontSizes
A vector of length 5. The first number is the size of the title.
The second number is the size of the axes titles. The third number is
the size of the axes values. The fourth number is the size of the
legends' titles. The fifth number is the font size of the legend labels.
colours
The colours to plot data of each class in.
plot
Logical. If TRUE, a plot is produced on the current graphics device.
Value
Plots.
Author(s)
Dario Strbenac
Examples
# First 25 samples are mixtures of two normals. Last 25 samples are one normal.
genesMatrix <- sapply(1:25, function(geneColumn) c(rnorm(50, 5, 1), rnorm(50, 15, 1)))
genesMatrix <- cbind(genesMatrix, sapply(1:25, function(geneColumn) rnorm(100, 9, 3)))
classes <- factor(rep(c("Poor", "Good"), each = 25), levels = c("Good", "Poor"))
chosen <- 1:5 # First five genes in the data were chosen.
plotFeatureClasses(genesMatrix, classes, chosen, expressionLimits = NULL)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
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R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(ClassifyR)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
rbind, rownames, sapply, setdiff, sort, table, tapply, union,
unique, unsplit
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Loading required package: BiocParallel
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/ClassifyR/plotFeatureClasses.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plotFeatureClasses
> ### Title: Plot Density and Scatterplot for Genes By Class
> ### Aliases: plotFeatureClasses plotFeatureClasses,matrix-method
> ### plotFeatureClasses,ExpressionSet-method
>
> ### ** Examples
>
> # First 25 samples are mixtures of two normals. Last 25 samples are one normal.
> genesMatrix <- sapply(1:25, function(geneColumn) c(rnorm(50, 5, 1), rnorm(50, 15, 1)))
> genesMatrix <- cbind(genesMatrix, sapply(1:25, function(geneColumn) rnorm(100, 9, 3)))
> classes <- factor(rep(c("Poor", "Good"), each = 25), levels = c("Good", "Poor"))
> chosen <- 1:5 # First five genes in the data were chosen.
>
> plotFeatureClasses(genesMatrix, classes, chosen, expressionLimits = NULL)
`stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
>
>
>
>
>
> dev.off()
null device
1
>
providing 
.
